Validating a Proteomic Signature of Severe COVID-19.

COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN: Prospective observational cohort study. SETTING: Two hospitals in the United States. PATIENTS: One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88-0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS: Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.

Validating a Proteomic Signature of Severe COVID-19

OBJECTIVES: COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN: Prospective observational cohort study. SETTING: Two hospitals in the United States. PATIENTS: One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88–0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS: Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.

Interactions of the protease inhibitor, ritonavir, with common anesthesia drugs.

The protease inhibitor, ritonavir, is a strong inhibitor of CYP 3A. The drug is used for management of the human immunovirus and is currently part of an oral antiviral drug combination (nirmatrelvir-ritonavir) for the early treatment of SARS-2 COVID-19-positive patients aged 12 years and over who have recognized comorbidities. The CYP 3A enzyme system is responsible for clearance of numerous drugs used in anesthesia (e.g., alfentanil, fentanyl, methadone, rocuronium, bupivacaine, midazolam, ketamine). Ritonavir will have an impact on drug clearances that are dependent on ritonavir concentration, anesthesia drug intrinsic hepatic clearance, metabolic pathways, concentration-response relationship, and route of administration. Drugs with a steep concentration-response relationship (ketamine, midazolam, rocuronium) are mostly affected because small changes in concentration have major changes in effect response. An increase in midazolam concentration is observed after oral administration because CYP 3A in the gastrointestinal wall is inhibited, causing a large increase in relative bioavailability. Fentanyl infusion may be associated with a modest increase in plasma concentration and effect, but the large between subject variability of pharmacokinetic and pharmacodynamic concentration changes suggests it will have little impact on an individual patient, especially when used with adverse effect monitoring. It has been proposed that drugs that have no or only a small metabolic pathway involving the CYP 3A enzyme be used during anesthesia, for example, propofol, atracurium, remifentanil, and the volatile agents. That anesthesia approach denies children of drugs with considerable value. It is better that the inhibitory changes in clearance of these drugs are understood so that rational drug choices can be made to tailor drug use to the individual patient. Altered drug dose, anticipation of duration of effect, timing of administration, use of reversal agents and perioperative monitoring would better behoove children undergoing anesthesia.

Pharmacokinetic Pharmacodynamic Modelling Contributions to Improve Paediatric Anaesthesia Practice.

The use of pharmacokinetic-pharmacodynamic models has improved anaesthesia practice in children through a better understanding of dose-concentration-response relationships, developmental pharmacokinetic changes, quantification of drug interactions and insights into how covariates (e.g., age, size, organ dysfunction, pharmacogenomics) impact drug prescription. Simulation using information from these models has enabled the prediction and learning of beneficial and adverse effects and decision-making around clinical scenarios. Covariate information, including the use of allometric size scaling, age and consideration of fat mass, has reduced population parameter variability. The target concentration approach has rationalised dose calculation. Paediatric pharmacokinetic-pharmacodynamic insights have led to better drug delivery systems for total intravenous anaesthesia and an expectation about drug offset when delivery is stopped. Understanding concentration-dependent adverse effects have tempered dose regimens. Quantification of drug interactions has improved the understanding of the effects of drug combinations. Repurposed drugs (e.g., antiviral drugs used for COVID-19) within the community can have important effects on drugs used in paediatric anaesthesia, and the use of simulation educates about these drug vagaries.

Characteristics, Outcomes, and Trends of Patients With COVID-19-Related Critical Illness at a Learning Health System in the United States.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic continues to surge in the United States and globally. OBJECTIVE: To describe the epidemiology of COVID-19-related critical illness, including trends in outcomes and care delivery. DESIGN: Single-health system, multihospital retrospective cohort study. SETTING: 5 hospitals within the University of Pennsylvania Health System. PATIENTS: Adults with COVID-19-related critical illness who were admitted to an intensive care unit (ICU) with acute respiratory failure or shock during the initial surge of the pandemic. MEASUREMENTS: The primary exposure for outcomes and care delivery trend analyses was longitudinal time during the pandemic. The primary outcome was all-cause 28-day in-hospital mortality. Secondary outcomes were all-cause death at any time, receipt of mechanical ventilation (MV), and readmissions. RESULTS: Among 468 patients with COVID-19-related critical illness, 319 (68.2%) were treated with MV and 121 (25.9%) with vasopressors. Outcomes were notable for an all-cause 28-day in-hospital mortality rate of 29.9%, a median ICU stay of 8 days (interquartile range [IQR], 3 to 17 days), a median hospital stay of 13 days (IQR, 7 to 25 days), and an all-cause 30-day readmission rate (among nonhospice survivors) of 10.8%. Mortality decreased over time, from 43.5% (95% CI, 31.3% to 53.8%) to 19.2% (CI, 11.6% to 26.7%) between the first and last 15-day periods in the core adjusted model, whereas patient acuity and other factors did not change. LIMITATIONS: Single-health system study; use of, or highly dynamic trends in, other clinical interventions were not evaluated, nor were complications. CONCLUSION: Among patients with COVID-19-related critical illness admitted to ICUs of a learning health system in the United States, mortality seemed to decrease over time despite stable patient characteristics. Further studies are necessary to confirm this result and to investigate causal mechanisms. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality.

Impact of COVID-19 on inpatient clinical emergencies: A single-center experience.

AIM: Determine changes in rapid response team (RRT) activations and describe institutional adaptations made during a surge in hospitalizations for coronavirus disease 2019 (COVID-19). METHODS: Using prospectively collected data, we compared characteristics of RRT calls at our academic hospital from March 7 through May 31, 2020 (COVID-19 era) versus those from January 1 through March 6, 2020 (pre-COVID-19 era). We used negative binomial regression to test differences in RRT activation rates normalized to floor (non-ICU) inpatient census between pre-COVID-19 and COVID-19 eras, including the sub-era of rapid COVID-19 census surge and plateau (March 28 through May 2, 2020). RESULTS: RRT activations for respiratory distress rose substantially during the rapid COVID-19 surge and plateau (2.38 (95% CI 1.39-3.36) activations per 1000 floor patient-days v. 1.27 (0.82-1.71) during the pre-COVID-19 era; p = 0.02); all-cause RRT rates were not significantly different (5.40 (95% CI 3.94-6.85) v. 4.83 (3.86-5.80) activations per 1000 floor patient-days, respectively; p = 0.52). Throughout the COVID-19 era, respiratory distress accounted for a higher percentage of RRT activations in COVID-19 versus non-COVID-19 patients (57% vs. 28%, respectively; p = 0.001). During the surge, we adapted RRT guidelines to reduce in-room personnel and standardize personal protective equipment based on COVID-19 status and risk to providers, created decision-support pathways for respiratory emergencies that accounted for COVID-19 status uncertainty, and expanded critical care consultative support to floor teams. CONCLUSION: Increased frequency and complexity of RRT activations for respiratory distress during the COVID-19 surge prompted the creation of clinical tools and strategies that could be applied to other hospitals.

Deep learning to detect acute respiratory distress syndrome on chest radiographs: a retrospective study with external validation.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a common, but under-recognised, critical illness syndrome associated with high mortality. An important factor in its under-recognition is the variability in chest radiograph interpretation for ARDS. We sought to train a deep convolutional neural network (CNN) to detect ARDS findings on chest radiographs. METHODS: CNNs were pretrained on 595 506 radiographs from two centres to identify common chest findings (eg, opacity and effusion), and then trained on 8072 radiographs annotated for ARDS by multiple physicians using various transfer learning approaches. The best performing CNN was tested on chest radiographs in an internal and external cohort, including a subset reviewed by six physicians, including a chest radiologist and physicians trained in intensive care medicine. Chest radiograph data were acquired from four US hospitals. FINDINGS: In an internal test set of 1560 chest radiographs from 455 patients with acute hypoxaemic respiratory failure, a CNN could detect ARDS with an area under the receiver operator characteristics curve (AUROC) of 0·92 (95% CI 0·89-0·94). In the subgroup of 413 images reviewed by at least six physicians, its AUROC was 0·93 (95% CI 0·88-0·96), sensitivity 83·0% (95% CI 74·0-91·1), and specificity 88·3% (95% CI 83·1-92·8). Among images with zero of six ARDS annotations (n=155), the median CNN probability was 11%, with six (4%) assigned a probability above 50%. Among images with six of six ARDS annotations (n=27), the median CNN probability was 91%, with two (7%) assigned a probability below 50%. In an external cohort of 958 chest radiographs from 431 patients with sepsis, the AUROC was 0·88 (95% CI 0·85-0·91). When radiographs annotated as equivocal were excluded, the AUROC was 0·93 (0·92-0·95). INTERPRETATION: A CNN can be trained to achieve expert physician-level performance in ARDS detection on chest radiographs. Further research is needed to evaluate the use of these algorithms to support real-time identification of ARDS patients to ensure fidelity with evidence-based care or to support ongoing ARDS research. FUNDING: National Institutes of Health, Department of Defense, and Department of Veterans Affairs.

Benchmarking Critical Care Well-Being: Before and After the Coronavirus Disease 2019 Pandemic.

OBJECTIVES: Examine well-being, measured as burnout and professional fulfillment, across critical care healthcare professionals, ICUs, and hospitals within a health system; examine the impact of the coronavirus disease 2019 pandemic. DESIGN: To complement a longitudinal survey administered to medical critical care physicians at the end of an ICU rotation, which began in May 2018, we conducted a cross-sectional survey among critical care professionals across four hospitals in December 2018 to January 2019. We report the results of the cross-sectional survey and, to examine the impact of the coronavirus disease 2019 pandemic, the longitudinal survey results from July 2019 to May 2020. SETTING: Academic medical center. SUBJECTS: Four-hundred eighty-one critical care professionals, including 353 critical care nurses, 58 advanced practice providers, 57 physicians, and 13 pharmacists, participated in the cross-sectional survey; 15 medical critical care physicians participated in the longitudinal survey through the coronavirus disease 2019 pandemic. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Burnout was present in 50% of ICU clinicians, ranging from 42% for critical care physicians to 55% for advanced practice providers. Professional fulfillment was less common at 37%, with significant variability across provider (p = 0.04), with a low of 23% among critical care pharmacists and a high of 53% among physicians. Well-being varied significantly at the hospital and ICU level. Workload and job demand were identified as drivers of burnout and meaning in work, culture and values of work community, control and flexibility, and social support and community at work were each identified as drivers of well-being. Between July 2019 and March 2020, burnout and professional fulfillment were present in 35% (15/43) and 58% (25/43) of medical critical care physician responses, respectively. In comparison, during the coronavirus disease 2019 pandemic, burnout and professional fulfillment were present in 57% (12/21) and 38% (8/21), respectively. CONCLUSIONS: Burnout was common across roles, yet differed across ICUs and hospitals. Professional fulfillment varied by provider role. We identified potentially modifiable factors related to clinician well-being that can inform organizational strategies at the ICU and hospital level. Longitudinal studies, designed to assess the long-term impact of the coronavirus disease 2019 pandemic on the well-being of the critical care workforce, are urgently needed.